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Hypothesis-driven classification of materials using nuclear magnetic resonance relaxometry

a nuclear magnetic resonance and relaxometry technology, applied in the direction of nuclear magnetic resonance analysis, measurement using nmr, instruments, etc., can solve the problems of difficult to distinguish hazardous materials from non-hazardous materials, different hazardous materials from each other, and measurement has a cost in required energy, result processing, time and other resources, and achieves low overall measurement cost, high degree of confidence, and the effect of costing the required energy

Active Publication Date: 2016-08-09
TRIAD NAT SECURITY LLC
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  • Summary
  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

In many cases, however, multiple materials have similar values for T1, T2, and T1ρ, making it difficult to distinguish both hazardous materials from non-hazardous materials and different hazardous materials from each other based on the NMR relaxation parameters alone.
Classification parameter values are obtained by performing measurements, and these measurements have a cost in required energy, result processing, time, and other resources.

Method used

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  • Hypothesis-driven classification of materials using nuclear magnetic resonance relaxometry
  • Hypothesis-driven classification of materials using nuclear magnetic resonance relaxometry
  • Hypothesis-driven classification of materials using nuclear magnetic resonance relaxometry

Examples

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example 1

[0030]An exemplary method 300 for determining the identity of a substance is illustrated in FIG. 3. Method 300 may be implemented at least in part by a computing device. A reference group of known materials is identified in step 302, the known materials having known values for classification parameters. The classification parameters comprise at least one of T1, T2, T1ρ, RNS, and LAC. In step 304, a measurement sequence is optimized based on at least one of a measurement cost of the classification parameters and an initial probability of the known materials in the reference group.

[0031]In some examples at least one classification parameter value is obtained for the substance according to the optimized measurement sequence. The at least one classification parameter value obtained for the substance may then be compared to classification parameter values of the known materials in the reference group to determine whether the substance is one of the known materials. In some examples, comp...

example 2

[0035]An exemplary method 400 for determining the identity of a substance is illustrated in FIG. 4. Method 400 may be implemented at least in part by a computing device. In step 402, a set of classification parameters for which values can be obtained is determined. The classification parameters comprise at least one of T1, T2, T1ρ, RNS, and LAC. In step 404, a measurement cost of each classification parameter in the set is determined. Measurement cost may relate to the time required to perform the measurement and obtain classification parameter values.

[0036]A reference group of known materials each having known classification parameter values is identified in step 406. In step 408, an initial probability is received for each material in the reference group. A plurality of measurement sequences for obtaining values for each of the set of classification parameters for the substance is determined in step 410. An aggregate cost for each of the plurality of measurement sequences is calcu...

example 3

[0039]An exemplary method 500 for building one or more substance identification tables for determining the identity of a substance are illustrated in FIG. 5. Method 500 may be implemented at least in part by a computing device. In step 502, a set of classification parameters for which values can be obtained is determined. The classification parameters comprise at least one of T1, T2, T1ρ, RNS, and LAC. In step 504, a measurement cost of each classification parameter in the set is determined. The measurement cost reflects the time required to obtain a measurement value of the classification parameter. In step 506, a reference group of known materials is identified. Classification parameter values for each of the known materials in the reference group are obtained in step 508. An initial probability is assigned to each material in the reference group in step 510. Such a substance identification table can be used to identify substances as discussed above with regard to methods 100, 200...

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Abstract

Technologies related to identification of a substance in an optimized manner are provided. A reference group of known materials is identified. Each known material has known values for several classification parameters. The classification parameters comprise at least one of T1, T2, T1ρ, a relative nuclear susceptibility (RNS) of the substance, and an x-ray linear attenuation coefficient (LAC) of the substance. A measurement sequence is optimized based on at least one of a measurement cost of each of the classification parameters and an initial probability of each of the known materials in the reference group.

Description

CLAIM OF PRIORITY[0001]The present application claims the benefit of U.S. Provisional Patent Application Ser. No. 61 / 637,987 filed on 25 Apr. 2012 and entitled “Hypothesis-Driven Classification of Materials Using Nuclear Magnetic Resonance Relaxometry,” the entirety of which is incorporated herein by this reference.STATEMENT REGARDING FEDERAL RIGHTS[0002]This invention was made with government support under Contract No. DE-AC52-06NA25396, awarded by the U.S. Department of Energy to Los Alamos National Security, LLC for the operation of the Los Alamos National Laboratory. The government has certain rights in the invention.BACKGROUND[0003]The identity of unknown substances can be determined through a variety of means. When samples of an unknown substance can be obtained, various tests and analyses can be performed to determine the likely identity of the substance. In some instances, however, it is either not possible or not desirable to obtain samples of an unknown substance. In such ...

Claims

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Application Information

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Patent Type & Authority Patents(United States)
IPC IPC(8): G01N23/04G01R33/44G01N24/08
CPCG01R33/44G01N24/084G01R33/448
Inventor ESPY, MICHELLE A.MATLASHOV, ANDREI N.SCHULTZ, LARRY J.VOLEGOV, PETR L.
Owner TRIAD NAT SECURITY LLC
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